Energy Systems Optimization Of A Shopping Mall

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Energy Systems Optimization of a Shopping Mall: The present study focuses on the development of software (general mathematical optimization model) which has the following characteristics:
• It will be able to find the optimal combination of installed equipment (power & heat generation etc) in a Shopping Mall (micro-grid)
• With multi-objective to maximize the cost at the same time as minimizing the environmental impacts (i.e. CO2 emissions).
• To date, this tool is scarce to the industry (similar to DER-CAM, Homer).

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Energy Systems Optimization Of A Shopping Mall

  1. 1. Energy systems optimization of a Shopping mall Aristotelis Giannopoulos 26/09/08 Supervised by: Prof. David Fisk (Civil and Environmental Engineering) Prof. Stratos Pistikopoulos (Chemical Engineering) A thesis submitted to Imperial College London in partial fulfilment of the requirements for the degree of Master of Science in Sustainable Energy Futures and for the Diploma of Imperial College Faculty of Engineering Imperial College London London SW7 2AZ, UK 1
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  3. 3. Table of Contents Table of Contents ...................................................................................................................... 3 List of Figures and Tables ......................................................................................................... 6 Glossary .................................................................................................................................. 11 Abstract ................................................................................................................................... 12 1. Introduction 1.1 Global Energy Consumption & Buildings contribution ........................................... 13 1.2 Decentralized energy systems ................................................................................... 14 1.3 Short plan and explanation of the model ................................................................... 16 2. Literature review 2.1 Energy Consumption in a Shopping Mall ................................................................. 19 2.2 Alternative Technologies and Energy sustainability in SM ...................................... 27 2.2.1 Description of the different technical alternatives ........................................... 27 2.2.2 Photovoltaic’s ................................................................................................. 28 2.2.3 Co-generation ................................................................................................... 28 2.2.4 Tri-generation model........................................................................................ 29 2.2.5 Gas boiler ......................................................................................................... 30 2.2.6 Grid Electricity and other parameters .............................................................. 30 2.2.7 Electric chiller .................................................................................................. 31 2.2.8 Absorption chiller ........................................................................................... 32 2.3 Distributed Energy Resources in SM and other Commercial Buildings ................... 32 3. Model inputs 3.1 Technology database ................................................................................................. 36 3.2 Shopping mall description ........................................................................................ 41 3.3 Tariffs inputs 3.3.1 Natural gas prices ....................................................................................... 46 3.3.2 Electricity prices (Grid) ............................................................................. 47 4. Mathematical Model 4.1 Introduction ............................................................................................................... 50 4.2 Mathematical Programming ...................................................................................... 50 3
  4. 4. 4.3 General Algebraic Modeling System (GAMS) ........................................................ 51 4.4 Model Description .................................................................................................... 52 4.5 Mathematical Formulation ........................................................................................ 54 5. Results 5.1 Scenarios and Sensitivities .............................................................................................. 59 5.2 Outline of results .............................................................................................................. 61 5.3 Overview of spot market prices results scenario .............................................................. 62 5.4 Assessment of specific cases 5.4.1 Case 1: Grid plus boiler ................................................................................... 67 5.4.2 Case 2: Without CHP/CCHP ........................................................................... 69 5.4.3 Case 3: Without CCHP .................................................................................... 71 5.4.4 Case 4: Final case ............................................................................................. 73 5.4.5 Case 5: PV plus Grid plus Boiler ..................................................................... 78 5.4.6 Case 6: At least seven PV ................................................................................ 81 5.4.7 Case 7: High carbon price ................................................................................ 88 5.4.8 Case 8: High carbon price with a 20% PV capital reduction ........................... 88 5.4.8 Case 9: 50% PV capital reduction .................................................................... 89 5.4.9 Case 10, 11: 50 % cheaper electricity prices, 50% more expensive NG ......... 89 5.5 Fixed electricity price scenario ........................................................................................ 90 5.5.1 Electricity price up to 0.08 $/KWh .................................................................. 91 5.5.2 Electricity price from 0.09 to 0.12 $/KWh....................................................... 91 5.5.3 Electricity price 0.13 $/KWh ........................................................................... 92 5.5.4 Electricity price 0.14$/KWh ............................................................................ 93 5.5.5 Electricity price from 0.15 to 0.49 $/KWh ....................................................... 94 5.5.6 Electricity price from 0.5 to 0.57 $/KWh......................................................... 96 Electricity price from 0.58 $/KWh ........................................................ 98 5.5.7 Conclusions ................................................................................................................. 99 6. 4
  5. 5. Bibliography ............................................................................................................... 104 Appendix .................................................................................................................... 106 5
  6. 6. List of Figures Figure 1, World Population distribution in urban and rural place……………………………..….13 Figure 2, Total London Energy use breakdown……………………………………………….….13 Figure 3, Electricity generation by fuel in US (IEA, World Energy Outlook, 2004)……………..14 Figure 4, graphic representation of the DGT-SM…………………………………………………17 Figure 5, technical alternatives used in this model………………………………………………..18 Figure 6, monthly electricity consumption profiles for the four shopping malls (Joseph C.Lam D. H., 2003)……………………………………………………………………...21 Figure 7, breakdown of the major end uses in the four shopping malls (Joseph C.Lam D. H., 2003)…………………………………………………………………..….22 Figure 8, measured hourly electrical load profiles for Building A………………………….…....23 Figure 9, measured hourly electrical load profiles for Building B…………………………..…....23 Figure 10, measured hourly electrical load profiles for Building C………………………………23 Figure 11, measured hourly electrical load profiles for Building D………………………………23 Figure 12, January Peak Load for Mall……………………………………………………………25 Figure 13, August Peak Load for Mall……………………………………………………………..25 Figure 14, Mall Week Load Shape………………………………………………………………...25 Figure 15, Mall Peak Load Shape………………………………………………………………….25 Figure 16, Mall Weekend Load Shape……………………………………………………………..26 Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM………………………………………………………………………………..27 Figure 18, Average costs and productivity of PV’s………………………………………………...28 Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006)……………………………33 Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006)…………………………………………………………………………………………….34 Figure 21, Annual savings, (Nan Zhou a *. C., 2006)………………………………………………34 Figure 22, Technology database (Firestone, 2004)………………………………………………….40 Figure 23, SM Electrical load (F. Javier Rubio, 2001)………………………………………...……44 Figure 24, SM Electrical-only demand……………………………………………………………...44 Figure 25, SM cooling demand……………………………………………………………………..45 Figure 26, SM Heating demand…………………………………………………………………….45 Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008……...46 6
  7. 7. Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008……………..….47 Figure 29, Contribution of distribution costs to electricity bill (Williams P. a., 2001)……………..48 Figure 30, Spot market electricity prices……………………………………………………………49 Figure 31, Grid electricity price with the distribution company revenue…………………………..49 Figure 32 Bill savings over grid + boiler basic scenario……………………………………………63 Figure 33, Carbon savings over basis grid + boiler scenario………………………………………..64 Figure 34, Energy payments to the grid…………………………………………………………….65 Figure 35, Capital investment cost (includes installation and fixed costs) (section results overview).65 Figure 36, Energy sales back to the grid (section results overview)………………………………..66 Figure 37, Net present value (all included) (section results overview)……………………………..66 Figure 38, Carbon Taxes (all included) (section results overview)…………………………………67 Figure 39, Natural gas payments (all included) (section results overview)…………………………67 Figure 40, Energy balance and economic result for the grid plus boiler case……………………….68 Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case)……………...…69 Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case)….69 Figure 43, Energy balance and economic results for without CHP/CCHP case…………………….70 Figure 44, Total electricity purchases from the grid (without CHP/CCHP case)……………….…71 Figure 45, Sales back to the grid (without CHP/CCHP case)………………………………………71 Figure 46, energy balance results (without CCHP case)……………………………………………73 Figure 47, economic results (without CCHP case)…………………………………………………73 Figure 48, energy balance results (final case)………………………………………………………75 Figure 49, economic results (final case)……………………………………………………………75 Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case)………76 Figure 51, NG-1000CCHP power generation for cooling end use loads (final case)………………76 Figure 52, NG-1000CCHP Recovered heat going to meet cooling demand (final case)…………..77 Figure 53, NG-1000CCHP Recovered heat going to meet heating demand (final case)…………..77 Figure 54, NG-1000CCHP Energy sales back to the grid (final case)………………………………78 Figure 55, energy balance results (PV plus grid plus boiler case)……………………………………79 Figure 56, economic results (PV plus grid plus boiler case)…………………………………………79 Figure 57, Total electricity purchases from grid (PV plus grid plus boiler case)……………………80 Figure 58, PV power generation for electrical-only end use loads (PV plus grid plus boiler case)…80 Figure 59, PV power generation for cooling end use loads (PV plus grid plus boiler case)……….81 7
  8. 8. Figure 60, energy sales back to the grid (PV plus grid plus boiler case)………………………..…81 Figure 61, energy balance results (at least 7 PV case)…………………………………………..…82 Figure 62, economic results (at least 7 PV case)……………………………………………………83 Figure 63, NG-1000CCHP power generation for electrical-only end use load (at least 7 PV case)..83 Figure 64, NG-1000CCHP power generation for cooling end use loads (at least 7 PV case)………84 Figure 65, 7 PV-100 power generations for electrical-only end use loads (at least 7 PV case)…….84 Figure 66, 7 PV-100 power generations for cooling end use loads (at least 7 PV case)…………….85 Figure 67, NG-1000CCHP recovered heat going to meet heating demand (at least 7 PV case)…….86 Figure 68, NG-1000CCHP recovered heat going to meet cooling demand (at least 7 PV case)…...86 Figure 69, NG purchased for meeting heating demand by direct-fire burning (at least 7 PV case)...87 Figure 70, Energy sales back to the grid by power generated from PV’s (at least 7 PV case)…….87 Figure 71, Energy sales back to the grid by power generated from NG-1000CCHP (at least 7 PV case)…………………………………………………………………………………………………88 Figure 72 (appendix), energy balance and economic results (high carbon price scenario)……….106 Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case)……107 Figure 74, economic results (High carbon price with a 20% PV capital reduction case)………….107 Figure 75, NG-1000CCHP power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)………………………………………………………………………….108 Figure 76, NG-1000CCHP power generation for cooling end use (High carbon price with a 20% PV capital reduction case)……………………………………………………………………………..108 Figure 77, PV’s power generation for electrical-only end use (High carbon price with a 20% PV capital reduction case)…………………………………………………………………………….109 Figure 78, PV’s power generation for cooling end use (High carbon price with a 20% PV capital reduction case)…………………………………………………………………………………….109 Figure 79, NG-1000CCHP recovered heat going to meet heating demand (High carbon price with a 20% PV capital reduction case)……………………………………………………………………110 Figure 80, NG-1000CCHP recovered heat going to meet cooling demand (High carbon price with a 20% PV capital reduction case)…………………………………………………………………….110 Figure 81, NG purchased to meet heating demand in a boiler (High carbon price with a 20% PV capital reduction case)……………………………………………………………………………………111 Figure 82, NG-1000CCHP power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)……………………………………………………………………111 Figure 83, PV-100 power generation for selling back to the grid (High carbon price with a 20% PV capital reduction case)………………………………………………………………………………112 Figure 84, energy balance results (50% PV capital reduction)……………………………………..112 Figure 85, economic results (50% PV capital reduction)……………………………………………113 Figure 86, NG-1000CCHP power generation for electrical-only end use loads (50% PV capital reduction)……………………………………………………………………………………………113 8
  9. 9. Figure 87, NG-1000CCHP power generation for cooling end use loads (50% PV capital reduction)………………………………………………………………………………………….114 Figure 88, NG-1000CCHP power generation for selling back to the grid (50% PV capital reduction)………………………………………………………………………………………….114 Figure 89, PV power generation for electrical-only end use loads (50% PV capital reduction)…115 Figure 90, PV power generation for cooling end use loads (50% PV capital reduction)…………115 Figure 91, NG-1000CCHP recovered heat going to meet cooling demand (50% PV capital reduction)…………………………………………………………………………………………116 Figure 92, NG purchased to meet heating demand by direct burning in boiler (50% PV capital reduction)…………………………………………………………………………………………116 Figure 93, recovered heat going to meet heating demand (50% PV capital reduction)…………117 Figure 94, PV power generation for selling back to the grid (50% PV capital reduction)………117 Figure 95, energy balance results (50% cheaper electricity prices case)…………………………118 Figure 96, economic results (50% cheaper electricity prices case)………………………………118 Figure 97, energy balance results (50% more expensive NG price case) ………………………..119 Figure 98, economic results (50% more expensive NG price case)………………………………119 Figure 99, graph representation of the model results for different electricity prices……………..90 Figure 100, economic results for electricity price less than 9p/KWh (Fixed electricity price scenario)……………………………………………………………………………………………119 Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………92 Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………92 Figure 103, energy balance and economic results (Electricity price from 0.09 to 0.12 $/KWh case)………………………………………………………………………………………………120 Figure 103, energy balance and economic results (Electricity price 0.13 $/KWh)………………121 Figure 104, NG-60 CHP total electrical production (Electricity price 0.13 $/KWh)……………122 Figure 105, Heating demand met by NG-60CHP (Electricity price 0.13 $/KWh)………………122 Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh)……………93 Figure 107, total electricity purchases from grid (Electricity price 0.14$/KWh case)……………123 Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case)……94 Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)………………………………………………………………………………………………94 Figure 110, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)………………………………………………………………………………………………123 Figure 111, energy balance and economic results (Electricity price 0.14$/KWh case)…………124 Figure 112, energy balance and economic results (Electricity price from 0.15 to 0.49 $/KWh)…125 9
  10. 10. Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from 0.15 to 0.49 $/KWh)………………………………………………………………………………95 Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49 $/KWh)……………………………………………………………………………………………96 Figure 116, energy balance and economic results (Electricity price from 0.5 to 0.57 $/KWh)…126 Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57 $/KWh)…………………………………………………………………………………………97 Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh)…………97 Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh)…………98 List of Tables Table 1, summary of the building envelops and HVAC designs, (Joseph C.Lam D. H., 2003)………21 Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003)……………..22 Table 3, summary of the buildings envelops and HVAC designs, (Joseph C.Lam D. H., 2003)……...22 Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001)…………………………………………………………………………………………………....29 Table 5, Costs (electricity, gas, and biomass) and also CO2 trading factor, (SEA/RENUE, 2006)……30 Table 6, Proportion of electricity supplied to the national grid from different sources, and associated CO2 emission factors, 2005……………………………………………………………………….……31 Table 7, CO2 factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006)…………………………………………………………….31 Table 8, CO2 equivalents of electricity and fuels (1998 data), (F, 2005)……………………………..33 Table 9, Underlying Assumptions……………………………………………………………………..39 Table 10, β and γ values………………………………………………………………………………..39 Table 11, Scenarios examined………………………………………………………………………….59 Table 12, Examined sensitivities……………………………………………………………………….60 10
  11. 11. Glossary DGT-SM: Distributed Generation Technologies Selection Model DGT: Distributed Generation Technologies SM: Shopping Mall DG: Distributed generation PV: Photovoltaic’s CHP: Combined Heat and Power CCHP: Combined Cooling Heat and Power BIPV: Building-integrated photovoltaic’s LCA: Life Cycle Analysis NG: Natural Gas GHG: Green House Gases HVAC: heating, ventilation and air-conditioning NPI: normalized performance indicators COP: Coefficient of performance OTTV: overall thermal transfer value GAMS: Generic Algebraic Modeling System FC: fuel cell MAISY: market analysis and information system O&M: operation and maintenance MMBTU: Million British thermal unit NPV: Net Present Value 11
  12. 12. Abstract The usage of distributed generation technologies (DGT) for on-site electricity, heating and cooling production gives great opportunities to commercial consumers to evade all the transmission, distribution, supply and other non-energy delivery costs. Additionally, the usage of DGT technologies close to the thermal load gives the prospect to utilize the waste heat (for heating and cooling purposes) from the electricity production and finally reach higher efficiencies of burning the fuel from the conventional centralized power station. Despite the previous two very important facts, the usage of DGT and especially CHP/CCHP in commercial level is nearly not existed. This denial for installing these distributed technologies is the bad economic results of some bad installed systems. In order one system like CHP to meet the customer demand cheaper than the mature centralized power stations, a very careful planning of the system needed in order to be utilized most of the waste heat which will compensate for the lower electrical output compared to conventional power station. Until now very few tools existed, which are able to make a careful planning of these small scale generation systems. In this thesis, a mathematical model developed in GAMS, which is able to address these decisions and planning problems commercial consumers face to install DGT. The models name is Distributed Generation Technologies Selection Model (DGT-SM), and it is a mixed-integer linear program. Given the customer load (electricity, cooling, and heating), market information (natural gas prices, electricity prices), technologies database (capital cost, lifetime etc) DGT-SM is able to find the optimum combination of DGT that minimize the annual customer energy bill while at the same time the model decides their capacities and operation schedule throughout the year. The model was tested in a commercial shopping mall under many different scenarios and sensitivities and the results indicate substantial economic savings for all the cases (over the already existing grid and boiler case). Most of them (except one) had also enormous carbon savings. For the final scenario, where all the technologies was available for installation, the technology chosen by the model was one MW combined cooling, heating and power (CCHP) natural gas engine. The results for this scenario were 51% annual energy bill savings and 17% carbon savings over the grid plus boiler basic scenario. 12
  13. 13. 1. Introduction 1.1 Global Energy Consumption & Buildings contribution According to world energy outlook the world’s primary energy needs in the Reference Scenario are projected to grow by 55% between 2005 and 2030, at an average annual rate of 1.8% per year (IEA, World Energy Outlook:China and India insights, 2007). The population will exceed the 9 billion (now 6 billion) until 2050 (IEA, World Energy Outlook, 2004), and more than 80% of global population will live in cities (Figure 1). As we can see and from the Figure 2 below, buildings (domestic & commercial) account for the biggest amount of energy consumed in a city, almost 60% of the total. As becomes obvious, in the future the energy consumption in the buildings will dramatically increase comparing with the present. Urbanization of China and India is a representative Figure 1, World Population distribution in example of the above fact. urban and rural places Between the buildings commercial sector seems to have a growing interest. Several numbers of existing cities going from industrialism to service oriented economies. The last gives clear signal for the upcoming big growth of the commercial sector. One existing example of this change is Hong-Kong which economy shifted from being manufacturing based to more service oriented financial Figure 2, Total London Energy use breakdown structure (Joseph C.Lam D. H., 2003). As a result, there has been rapid development in many large scale commercial building projects. It becomes obvious that the result of this transition is more energy consumption in commercial buildings. Out of all the buildings, Shopping Malls, is a rapidly growing sector which until now very little research has been done and it is very interesting area because of the high electricity consumption per 2 compared to the other commercial buildings (Joseph C.Lam D. H., 2003). 13
  14. 14. Until now building demand met in a very inefficient way, both electricity supply (which consumed in buildings) and electricity demand. Electricity for the cities produced in power plants with a mean efficiency 30% (Tester W. Jefferson, 2005). More over this electricity consumed in the buildings in inefficient appliances such as light bulbs (3-5% efficiency), badly designed air-conditioned systems etc (Tester W. Jefferson, 2005). 1.2 Decentralized energy systems According to Lovins and Gumerman there is great potential for benefits from moving our economy from the centralized to a more distributed power generation model (Gumerman, 2003) (Lovins, 2002). Some concepts of decentralizing which are common and used systematically are micro-grids or distributed generation technologies (DGT) etc. All these concepts have differences between of them, but at the same time all of them agree that is a great need for our economies to decouple themselves as much as is possible from fossil fuels (e.g. renewable) or if this is not feasible for the near future at least to try to minimize the losses (e.g. unutilized heat). As we discussed in the previous section the greatest energy consumers of our economies in total are cities, where the greatest needs associated with the electricity consumption (e.g. cooling, lighting). The losses in electricity production (when fossil fuels are used) are mainly heat loses, heat losses which are growing if we consider the continuing increase of the electricity consumption worldwide. A characteristic example of this inefficiency is the case of USA, as can be easily noted in figure 3. Figure 3, (IEA, World Energy Outlook, 2004) 14
  15. 15. As can become obvious from this graph the losses are huge and going hand in hand with the electricity needs. The common logic says that these inefficiencies are a very good starting point for our economies to start moving to a more ‘’Sustainable Energy Future’’. For a more sustainable and green future except the renewable energy technologies key role can and must play the Combined Heat and Power (CHP) technologies. The biggest advantage of CHP (commercial use) is that can utilize the waste heat due to the fact that is close to the customer load, compared with the common power stations which are far from the end-user and cannot use this heat. The last happen due to the fact that the low-grade heat cannot travel like the electricity without significant loses. These technologies are common in industrial places but in order to make a big difference worldwide this technology must be applied and penetrate successfully in a commercial level (shopping malls, hotels, houses etc). Successful penetration of CHP in commercial level needs the acceptance of the people which means that must have a better economic result (also take into account the environmental effect) compared to the current conventional way of power production. The greatest challenge a CHP faces in a commercial level is the need to utilize a high amount of waste heat in order to reach high efficiencies and be economically feasible compared to the state of the art centralized power stations (economies of scale). This power and heat match becomes even more difficult if we thing the high volatility in buildings requirements driven by the working hours, electricity tariffs, fuel cost and weather. The last great challenges for scheduling and control in the commercial use of CHP was the spark for this project. Self-generation big advantage except the advantage of utilization of heat (if exist) is the avoidance of transmission and distribution of the electricity which typical account almost for the 50% of the final energy bill (Williams P. a., 2001). For most of the commercial buildings the electricity cost is much higher than the heating cost and the potential energy bill savings will come from the provision of the electricity and not from the heat. Due to the fact as mentioned before that the centralized power stations have bigger efficiencies for electricity production (less waste heat in analogy) it becomes obvious that the high utilization of heat for heating or cooling purposes is a 15
  16. 16. must. In order this to happen the commercial building must have except from electricity needs and high heating or cooling (use absorption chillers) loads. Bearing in mind the two previous facts shopping mall (SM) seems an ideal solution for many reasons. First of all SM are in particular consuming more energy than the other buildings and appear increasing across the world. Moreover, due to the variety of different stores and the nature of a SM (great cooling and lighting demand) there is a good ratio of electrical and heating loads, if we consider that the cooling demand can be covered with absorption chillers driven by heat. Another great advantage of a shopping mall is that during the working hours of year have an almost flat electrical load profile and a relatively high load profile all the off-working hours (e.g. high refrigeration demand during night). Until now very few methods are available for optimizing operation of commercial scale CHP, especially under variable fuel prices, and with the burden of small-scale diseconomies. Taken into account the grade importance of CHP (and generally the distributed generation technologies) in commercial scale in this report will developed a method for jointly optimizing heat and electricity production and use within a cost- minimizing framework while taking into account the carbon emissions. 1.3 Short plan and explanation of the model The present study focuses on the development of a general mathematical optimization model, with name Distributed Generation Technology Selection Model (DGT-SM), in GAMS (General Algebraic Modeling System) which will be able to minimize the energy payments of a shopping mall while minimize the environmental effect (CO2). In other words DGT-SM is able to make a Shopping Mall more ‘’Sustainable’’ while at the same time don’t compromise any comfort and meeting all the cooling, heating and electrical demand. In order DGT-SM to achieve this goal we must provide as data: Technologies (figure 2) information, market information and finally customer information. After the optimization the model will give as outputs: optimal technology combination, 16
  17. 17. operating schedule as well as and some other outputs (e.g. energy bill cost, CO2 etc). Figure 4 gives a graphic representation of the DGT-SM and figure 5 gives the technical alternatives which will be used in this version of the model. In chapter 3 will be explained in more detail the inputs of the model, in chapter 4 will be given and explained thoroughly the mathematical model while in chapters 5 and 6 will discussed the results and some conclusions on them. Figure 4, graphic representation of the DGT-SM 17
  18. 18. Demand Source Generation Conversion Technologies Technologies s GRID Electricity- Electricity only PV VC air cooled VC water cooled CHP Natural Gas Cooling Absorption Cooling Boiler Heating Waste Heat Figure 5, technical alternatives used in this model 18
  19. 19. 2. Literature Review Previous works have been selected and reviewed based on the relevance to Energy Consumption in a Shopping Mall, Distributed Energy resources in Shopping Malls and big Commercial Buildings in General, Alternative Technologies and Energy sustainability in SM. The related journals have been summarized with the problem, method used, and how successful the work was. Also, some of the definitions and introduction of basic principles of urban energy Buildings modeling and Optimization are presented. 2.1 Energy Consumption in a Shopping Mall In order to be able to see and compare the different options for meeting and decreasing the demand in SM it is important to understand and become familiar with the actual needs of this type of building first. Despite the fact that SM penetrating the building market in a very fast pace, few studies have been done as regards the electricity characteristics in shopping malls. According to energy audits and surveys which have been made for commercial air-conditioned buildings by the University of Canberra (Lam JC, 1995), air-condition account for 40-60% of the total electricity consumption with lighting in the second place accounting for the 20-30%. For a shopping mall these two factors become even more important if we consider the population density and the larger lighting load and, hence, the higher air-conditioning needs compared with the common commercial buildings. Until now the needs of commercial buildings covered from grid as regards the electricity and from boilers (natural gas, diesel) as regards the heating. As becomes obvious from now on this scenario will be the common or base case. Below, will be exhibited, some previous works as regards the demand and the loads in shopping malls and other with similar needs commercial buildings. One good approach in analyzing the consumption characteristics in shopping malls in subtropical climates was made in China by the City University of Hong Kong (Joseph C.Lam D. H., 2003). The objective of this study was to investigate the electricity use characteristics in shopping centers in subtropical Hong Kong. The four buildings 19
  20. 20. examined in this study are fully air-conditioned and was made during the 1990s. The table 1 below summarizes the main characteristics of the buildings envelope. Twelve months electricity consumption data were gathered for each of the four shopping centers. The monthly electricity consumption for the different shopping mall’s showed in Figure 6. As was presumable the electricity consumption peaks during the summer period due to the hot summer months and the air-conditioning needs. During the mid-season the electricity consumption is also high due to the high internal loads, such as people, office but mainly the thermal loads from the artificial light. The next information was takes was the breakdown of the four major electricity end uses in percentages (Figure 7). In order to breakdown this total electricity consumption the following method was followed. For lighting consumption, the number of light fixtures and their corresponding power ratings in both the landlord and tenants areas were surveyed and estimated wherever appropriate. Then taking into account the daily operating hours, the electricity consumption for lighting was determined. A similar approach was adopted for the electrical appliances consumption. For the escalators and the lifts were used energy analyzers (DRANETZ 8000-2) in order to measure the electricity consumption. The HVAC consumption was obtained by subtracting the total electricity consumption from the other three. The biggest consumer was the HVAC system, with percentages 47 to 54 of the total consumption. Lighting accounted for the 33-38% and with average lighting load densities for the landlord and tenants areas 15 and 55 2 , respectively. On average, HVAC and lighting accounted for about 85% of the total building electricity use. Finally in table 2 showed the normalized performance indicators (NPI), which defined as the electricity use per unit floor area. For the landlord and tenants the consumption were 485–795 2 (landlord area only) and 294–327 2 (tenants area only), respectively. The total annual electricity use per unit gross floor area was from 391–454 2 , with a mean NPI of 430 2 . In conclusion, we can say that this report gave a good indication of the electricity consumption characteristics of SM in subtropical climates. Of course the number of the shopping mall was limited; the sub-metering wasn’t 100% accurate due to the lack of all tenants’ data. The breakdown of major electricity end uses was estimated 20
  21. 21. using only the non-weather sensitive loads (lighting, appliances etc) and finally, they didn’t give more specific data for the electrical loads (cooling, lighting etc) during the days of a normal week and for different seasons of a year (summer, winter etc). Table 1, summary of the building envelop and HVAC designs, (Joseph C.Lam D. H., 2003) Figure 6, monthly electricity consumption profiles for the four shopping malls, (Joseph C.Lam D. H., 2003) 21
  22. 22. Figure 7, breakdown of the major end uses in the four shopping malls (Joseph C.Lam D. H., 2003). Table 2, summary of annual electricity per unit floor area (Joseph C.Lam D. H., 2003) Office building 1 Office building 2 Office building Office building 3 4 Number of storeys Multi-tenant 18 22 18 Total gross floor area ( ) 22.000 10.000 29.000 9.000 Curtain walling Building envelope Inserted windows Inserted RC structure Window-to-wall ratio windows 50% 50% 60% (WWR) 20% Glazing type Single tinted glass single reflective Single clear Single tinted Shading Coefficient 0.7 glass glass glass 0.3 0.9 0.6 HVAC plant/equipment Air side system PAU/Fan-coil unit PAU/Fan-coil unit Ceiling-mounted Fan-coil unit Chiller type Hermetic Variable air fan coil centrifugal volume (VAV) Constant air- VAV volume Heat rejection method Air-cooled Air-cooled Sea water- Air-cooled cooled Chiller COP (kWr 3 3 5 3 output/kWe input) Table 3, summary of the buildings envelop and HVAC designs, (Joseph C.Lam D. H., 2003) 22
  23. 23. In a second study made in air- conditioned commercial/office buildings (Joseph C.Lam D. H., 2003), almost the same results were takes as before. The buildings characteristics are given in the above table 3. In this study the hourly load profiles was monitored Figure 8, measured hourly electrical load during the hot months of July and August profiles for Building A and the results for the four buildings (A, B, C, D) showed in the figures 8, 9, 10, 11. The results show that HVAC was the larger electricity end user, accounting for 30-60% of the total electrical demand during the office hours. Lighting came in the second place Figure 9, measured hourly electrical load profiles for Building B accounting for the 20-35% of the total electrical demand. Small power for a 15-25% with lifts in the last place with only few percentages mainly in peak hours. During the office hours (08:00 – 18:00) the variation was up to 10%, which occurred mainly between 12:00- 15:00 when peak demand was reached. The Figure 10, measured hourly electrical load profiles for Building C major consumer between the HVAC systems was the chiller which consumes the 70% of the HVAC consumption (or 40% of the total electric load). In this study was suggested a chiller load shifting in the night using thermal chilled store if it is Figure 11, measured hourly electrical load profiles for Building D 23
  24. 24. economically feasible. Concluding, from the previous study we noticed that the electrical needs for big commercial office buildings don’t defer that much with the shopping mall demand. Both have the same marginal needs in HVAC and lighting (in summer) and of course they have almost the same electrical load profiles. This derives from the fact that both have many commons. They have same working hours, almost the same building envelop, and finally are in the same climate. Of course they have and some differences such as lighting loads and people densities. In a shopping mall the lighting loads are much higher (20-50 W/2 ) than in an office (12-25 W/2 ) which not only cause a higher electrical need but also cause and higher thermal loads, which means higher cooling loads. Moreover the higher occupancy density causes the need of higher cooling loads and in humid climates we have the humidity more easily in the building (also cooling problem). Another important difference is that in the night the shopping mall has bigger electrical loads, comparing with the peak demand, due to the refrigeration needs from the food stores. 24
  25. 25. In the CERTS Customer Adoption Model paper (F. Javier Rubio, 2001) examine the use of distributed energy sources in a Mall and give the electrical loads of them. According to these data the ratio of minimum to maximum load is smaller in January than it is Figure 12, January Peak Load for Mall in August (0.31 in January and 0.53 in August). This implies that the difference between minimum load and the peak is more evident in January (Figure 12) than in August (Figure 13). The seasonal differences in the shape of the Figure 13, August Peak Load for Mall profiles are obvious in the two figures (12, 13). In January (Figure 12) there is a high level of load demand from approximately 9:00 to 22:00, and then the demand drops dramatically to the low level (these are the mall working hours). On the other hand, August (Figure 13) has a peak in the profile at around 15:00 (during the hottest part of the day). In all other hours, the load declines to or rises from the level that is maintained from around 22:00 to 10:00. The load factor for this customer is 0.36, pretty low, showing that the peaks are well above the average load demanded (686 kW). At the below figures 14, 15, 16 we can see the week, peak, and weekend loads for the different months of the year during the day. Figure 14, Mall Week Load Shape Figure 15, Mall Peak Load Shape 25
  26. 26. Other papers attempts to analyze the Electricity consumption of Commercial buildings are the: Electricity use characteristics of purpose-built office buildings in subtropical (Joseph C. Lam *, 2004), a study of energy performance of hotel buildings in Hong Kong (Deng Shi-Ming, 2000) For a specific site, the source of end use energy Figure 16, Mall Weekend Load Shape load estimates is typically building energy simulation using a model based on the DOE-2 engine, such as eQUEST, or the more advanced but less user-friendly EnergyPlus. These tools can calculate the hourly energy loads and costs of several types of commercial buildings given information about: building location, construction, operation, utility rate schedule, heating, ventilating, air- conditioning (HVAC) equipment, and finally distributed generation unit performance parameters and operation strategy. Concluding, Shopping Malls are large energy consumers, with energy consumption per 2 larger than the majority of the commercial buildings. The main energy need in a SM is electricity for cooling and lighting. Especially the cooling requirements are large due to the high density of people during the working hours and the high thermal loads from the artificial lighting inside the building. Also and the light requirements are high due to the special needs of a SM. Until now very little work has been done in SM as regards the energy optimization and sustainability in adverse with the large amount of papers existing for other commercial buildings. In the next section will be introduced the different alternative technologies can be used in SM. What are the future challenges? As becomes obvious from the existing analysis buildings due to their increasing contribution in the global energy consumption and their inefficient way they meet their demand until now there is a lot of potential to both decrease and meet the demand in a different more efficient way. In other terms, the objective is the Sustainable Development of the Buildings and especially in this case Shopping Malls while the comfort level of these buildings remains constant. 26
  27. 27. 2.2 Alternative Technologies and Energy sustainability in SM 2.2.1 Description of the different technical alternatives In the next figure 17, we can see some of the most important technical alternatives can be used in a SM to meet the electricity and heat demand. As we can see from the figure for electricity the alternatives are: Grid, Photovoltaic’s (PV), Combined Heat and Power (CHP, natural gas). For heat the alternatives are: CHP and boiler. For cooling we can use both electricity or/and heat in a VC cooled air condition and in an Absorption cooling system respectively. A short introduction and description of the above technologies are listed below. Sources Generation Conversion Demand Technologies Technologies GRID Electricity- Electricity only PV VC air cooled CHP VC water cooled Natural Gas Cooling Absorption Cooling Boile r Heating Waste Heat Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM 27
  28. 28. 2.2.2 Photovoltaic’s Solar radiation can be converted directly into electricity using photovoltaic (PV) cells. The electrical efficiency of PV is between 5-15%, and the energy output of such a system depends from the solar radiation, for UK the radiation range between 800- 1000 kW h (Northern to Southern England). According to the common technologies the installed cost of a BIPV is about 500 pounds per for roof tile and 900 pounds per for the most expensive facades (F, 2005). One squared meter of mono-crystalline array will produce roughly 150 kW h per year, and also for each kW installed will produced about 700 kW h per year. The maintenance and operation cost of a PV system is too low that is not included, and the lifetime is in average about 30 years. Finally, PV is almost ‘emission-free’, because there is no need for fuel or cooling water; it operates silently and is believed to fit in urban development. One kW panel can save 0.1 to 1 tonne of emitted per year. However, the manufacture of PV requires a lot of energy and is embodied some (F, 2005). The above prices summerized in the 18 Figure. Capacity Energy output kWh kWh per year for Cost per in each kW installed pounds pa 1kW 500 - 900 120 - 150 560 - 700 Figure 18, Average costs and productivity of PV’s 2.2.3 Co-generation Co-generation is also called combined heat and power (CHP). CHP in contrast with conventional power plants uses heat that is normally discarded to produce thermal energy, which can be provided to district heating systems, with result to reduce2 emissions and running costs. The efficiency depends from the type, scale and operation of the CHP with an average of 70-80% (25-35% electricity and 45-55% high grade or useful heat 71-82 c) (F, 2005). The different types of CHP are: Micro turbines, Fuel cells, Reciprocating engines, Gas turbines (simple-cycle cogeneration), Gas and steam turbines (combined-cycle cogeneration) and gas engines. Some data about those (capital cost, efficiency, power to-heat ratio, emissions etc) are represented in the table 4. Interesting issue is the operation of CHP’s, because in cogeneration it is important to optimize the balance of heat and electricity generation. This balance depends on the customer loads (electrical and thermal) and is possible 28
  29. 29. the CHP to follow the thermal or the electrical load. Other option is to produce more electricity and/or heat and sell it back to the grid/customer in order to have some profit. One other option is the fuel, natural gas or biomass. All the above options and other must be taken into account and optimized in the CHP installation to meet the same demand with less cost and emissions. Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001) 2.2.4 Tri-generation model Tri-generation is also known as combined heating, cooling and power generation or CHCP. CHCP uses the waste heat from CHP not only to meet the heat but also the cooling demand by applying the heat to absorption chillers. This chiller utilizes the heat to increase the pressure of refrigerant instead of using compressors which highly consume electricity. All the facts from co-generation also existing here, with more complexity because the optimization problem now extended further more. The advantage of the CHCP compared to co-generation becomes clear in buildings with high cooling demand like in this case in a Shopping Mall. 29
  30. 30. 2.2.5 Gas boiler A boiler is a device for generating steam for power, processing, or heating purposes or for producing hot water for heating purposes or hot water supply (used until now for the majority of the buildings). It provides the building with heating and hot water with efficiencies between 80-90% (Tester W. Jefferson, 2005) and can burn natural gas or biomass. A great disadvantage of the boiler is the ``bad`` use or degradation of high quality fuels like natural gas for the production of low grade heat for heating needs comparing with the CHP which use the same fuel to produce some high quality energy source (electricity) and some low grade heat. 2.2.6 Grid Electricity and other parameters The cost of electricity, gas and biomass are given in the table 5 for domestic commercial and wholesale use and also the 2 trading factor. In table 6 depicted the average 2 emissions factor for the total UK grid mix (g/kWh) and in table 7 are given values about the kg 2 emitted per kWh produced for the natural gas, boilers, renewables, and grid. Also in the table 7 are given prices about the inflation, discount rate etc. Table 5, Costs (electricity, gas, and biomass) and also trading factor, (SEA/RENUE, 2006). 30
  31. 31. Table 6, Proportion of electricity supplied to the national grid from different sources, and associated emission factors, 2005. Table 7, factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006) 2.2.7 Electric chiller The majority of the shopping malls use Vapor compression (VC, base scenario) with air-cooled chiller for air conditioning. The electric chiller is defined by its efficiency which expressed by the coefficient of performance (COP). The bigger is the COP the more efficient is the electric chiller with result the decrease of the electricity used (for the same comfort) and consequently the reduction of the fuel used (to produce electricity) and the emissions going into the environment. Most of the HVAC systems used in the shopping malls until now have a COP 3, but there are already existing vapor compressions with water-cooled chiller systems in the market with COP 5. 31
  32. 32. 2.2.8 Absorption chiller The alternative choice of the VC is the absorption cooling (AC) with absorption chiller (COP 1.2, heating) (Tester W. Jefferson, 2005). Absorption chillers use heat instead of mechanical energy to provide cooling. A thermal compressor consists of an absorber, a generator, a pump, and a throttling device, and replaces the mechanical vapor compressor. The basic cooling cycle is the same for the absorption and electric chillers, but the basic difference between the electric chillers and absorption chillers is that an electric chiller uses an electric motor for operating a compressor used for raising the pressure of refrigerant vapors and an absorption chiller uses heat for compressing refrigerant vapors to a high-pressure. The rejected heat from the power- generation equipment (e.g. turbines, micro turbines, and engines) may be used with an absorption chiller to provide the cooling in a CHP (Combined Heat and Power) system. The interesting part is to see through the optimization if it is more economic and environmentally feasible to operate a CHP with higher electric to thermal ratio in order to produce more electricity which will be used by an electric chiller in order to meet the cooling demand or is better to operate the CHP in a higher thermal to electric ratio in order to drive the heat through an absorption chiller and produce in this way the cooling demand. 2.3 Distributed Energy Resources in Shopping Malls and Commercial Buildings Many researchers have been conducted until now as regards the passive design of the building and the potential for reducing the demand (electricity, heating), but very few have been done as regards the different ways to meet this demand (e.g. renewable, CHP etc) in a Commercial building and especially for Shopping Mall less than five. As regards the Shopping Mall until now there is no paper which use a simulation or model optimization tool to integrate different distributed energy resources (more than one e.g. PV CHP) in it. For other Commercial Building like hospital, big offices etc, there are studies with the majority of them examine only one energy source (e.g. 32
  33. 33. PV) and not a combination of them, and in the case they examine more than one usually they do an exhaustive case by case simulation (no global optimum guarantee). One other fact is that most of the studies are not develop an energy optimization model but they use the existing commercial tools to examine different buildings. Furthermore from the energy optimization models existing, most of them focused only in some technologies (e.g. only in photovoltaic’s, or only in micro-turbine CHP, or only efficiency techniques etc) and in some aspects (e.g. only economic benefits or only environmental benefits examined but not both etc) of using decentralized energy resources in buildings. Until now no research has been done in which will examined different ways of meeting the demand and decreasing at the same time the demand of a building (without take into account the passive design of the building), with final objective not only the economic but also and the environmental benefit. One of the interesting studies was conducted in Japan by Nan Zhou (Nan Zhou a *. C., 2006). The objective was to find the best distributed energy resource system for different types of commercial buildings (hospital, big office, hotel sport facilities and retail) with constraint to meet the energy demands. In order this to be achieved was used an information base with different distributed technologies, Japanese energy tariffs and fuel prices, and the buildings needs which have been developed. Three scenarios were taken for each building type. The first scenario was to take no action in order to take the baseline (grid, NG boiler) costs, consumption and emissions. The second scenario made available to purchase a generation technology only for electricity production (without heat recovery and absorption cooling), and the third scenario was included everything (generation, recovery and with waste heat cooling). The results show a significant increase in the efficiency (Figure 19), decrease in carbon emissions (Figure 20) and finally decrease in annual energy cost (Figure 21). The results show a great potential and a very promising payoff (between 3 - 6.8 years). Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006). 33
  34. 34. Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006) Figure 21, Annual savings, (Nan Zhou a *. C., 2006) In the next paper Medrano (M. Medrano, 2008) try to investigate the economic, energy-efficiency, and environmental impacts of the integration of distributed technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar panels) into four representative generic commercial buildings (office building, medium office building, hospital, and college/school), using as simulation tool the DOE-2.2- derived user-interface eQUEST program. This tool can calculate the hourly energy loads and costs of several types of commercial buildings given information about: building location, construction, operation, utility rate schedule, heating, ventilating, air-conditioning (HVAC) equipment, and finally distributed generation unit performance parameters and operation strategy. The methodology Medrano follow have four steps. First, is the base case where n DG are included and during this step the electric and gas hourly profiles for days corresponding to peak electric and gas consumption are analyzed. In the second step are introduced and implemented different cost effective energy efficiency measures 34
  35. 35. day lighting, exterior shading, and improved HVAC performance) according to (e.g., energy use intensity with objective to reduce energy consumption and emissions. In the third case different DG technologies integrated in the buildings with the constraint that the waste heat utilized only for hot water and/or space heating. In the last approach, the traditional HVAC systems were replaced by heat driven absorption chillers alternatives, systems which works with hot water loops. In this way the thermal loads are utilized with result the increase of the overall efficiency of the DG system. Finally, the influences of utility gas and electric tariffs and weather conditions are illustrated, comparing the DG economic viability of the same office building in two U.S. locations. According to this paper the results gave a promising potential of the DG in these types of buildings. But I won’t stay in these results but in the methodology and the tools Medrano used in this report. Using this kind of simulation tools like eQUEST he investigates case by case combinations of DG in buildings, with the result not to find the optimum solution for cost reduction and environmental benefits and efficiency maximization. 35
  36. 36. 3. Model Inputs In this section will be presented and explained all the different inputs to the model. First will be explained the technology database, then the shopping mall loads and finally the market inputs. 3.1 Technology database In this section will be presented and explained the technology database that was used as input to our model. These dada depicted in figure 22 was initially produced by the National Renewable Energy Laboratory (NREL) in the study ‘’Gas-Fired Distribution Energy Resource Technology Characterizations’’ (Goldstein, 2003), and then further developed by Ernest Orlando Lawrence Berkeley National Laboratory in 2004 report Distributed Energy Resources Customer Adoption Model Technology Data (Firestone, 2004). This technology database contain information for the technologies: fuel cells (FC), gas turbines (GT), micro-turbines (MC), natural gas engines (NG), and photovoltaic’s (PV). Each technology described by a number of parameters, parameters which are inputs to the model and are explained below:  Capacity (maxp): This represents the maximum electrical output of the machine in KW.  Lifetime (years): is the average life of the machine in years.  Capital cost (capcost): includes the machines cost, the system design and finally the installation cost. This parameter defined as the cost per KW electrical output capacity ($/KW). These machines can be purchased: a) Without heat recovery potential (no CHP) b) With heat recovery for heating purposes (CHP) c) With heat recovery for both heating and cooling (CCHP) 36
  37. 37.  Operation and Maintenance Fixed Costs (OMFix): OMFix includes all the fixed annual operation and maintenance costs ($/KW per annum) (excludes fuel costs)  Operation and Maintenance Variable Costs (OMVar): OMVar includes all variable operation and maintenance costs ($/KWh) (excludes fuel costs)  Heat rate (HeatR): is the equipment heat rate (kJ fuel/KWh). Heat rate is linked to electrical efficiency, E by the equation: 3600 ℎ HeatR = HeatR in expressed with esteem to the higher heating value (HHV) of natural gas, due to the fact that the purchase of NG is with respect to the HHV  Heat to power Ratio (α): α is the ratio of recoverable heat per KWh electrical produced (maxp to maxp). According to Firestone, α value is based on the waste heat energy content prior to conversion via a heat exchanger, and here referred as recoverable heat (e.g. 1 KWh recoverable heat doesn’t cover 1 KWh heating demand but 1KWh x heat exchanger efficiency).  Conversion Efficiency for Recoverable Heat to Load Displacement (γ): γ value is an estimate of the portion of the recoverable heat that is useful and can displace real heating or/and cooling loads. γ value for heating is 0.8 and is actually the heat exchanger efficiency. Cooling loads according to Firestone are defined as the amount of electricity required to give the amount of cooling needed (assuming a specified value for electric chiller efficiency). γ for absorption cooling is consequently the ratio of electrical cooling load displacement to recoverable heat. This must take into account the heat exchanger efficiency in addition to the relative performance of electric and absorption chillers as described in the below 37
  38. 38. equation (where the COPabs is the coefficient of performance of an absorption chiller and COPelectric is the coefficient of performance of an electric chiller). COP abs γabs = EfficiencyHeatExanger * COP electric COPabs has value 0.65 for single-stage hot-water fired absorption chillers and COPelectric has value 4 for electric compression driven chillers. Thus, γabs has a value of 0.13 for CCHP (Firestone, 2004). The γ values for different end-uses are shown in table 10.  Conversion Efficiency for Fuel to Load Displacement (β): β is an estimate of the portion of the fuel energy content that is useful for displacing heat loads with the use of heat exchanger or/and cooling by the use of absorption chillers. β value for heating is 0.8 (boiler efficiency) and for cooling 0.13 as before. The lower value for cooling is due to the fact that cooling loads are expressed as the amount of electricity requested to provide the wanted amount of cooling and cooling data is invariably expressed as electricity used by the air conditioner. Thus, β for absorption chillers must incorporate the ratio of fuel energy to useful heat as well as the relative performance of electric and absorption chillers as discussed before (Firestone, 2004). The β values are depicted in table 10, while the table 9 summarizes the assumptions used for the β and γ values. 38
  39. 39. Table 9, Underlying Assumptions (Firestone, 2004) Table 10, β and γ values (Firestone, 2004) 39
  40. 40. Figure 22. Technology database (Firestone, 2004) 40
  41. 41. 3.2 Shopping mall description In this section, we are going to describe the shopping mall load profiles (electrical- only, cooling and heating). The most difficult part through this study was to find real 24 hour load profiles for SM’s due to the fact that these profiles either must calculated from a company (in response to a customer) or to produced by simulation tools like EnergyPlus or DOE-2, tools that wasn’t available in this MSc course boundaries. For that reason, ready electrical loads profiles were taken from the CERTS Customer Adoption Model paper (F. Javier Rubio, 2001). This shopping mall is located in southern California and the profiles were extracted from Maisy from the year 1998 data for the state of California. These data were reproduced and depicted in figure 23. Someone can claim that the SM in California has many differences with a SM in UK and thus the existed load profiles can’t be input to this report. But here this isn’t actually the case for two reasons: First, SM’s are a very specific consumer with especially large energy demand for cooling and lighting. From the previous two, only the cooling could have great differences between a building from California to London (due to climate differences), but actually in the SM this is not happening because the thermal loads that must be removed from a SM usually come not that much from the outside thermal mass transfer but mainly from the high density of people during the working hours and the high thermal loads from the artificial lighting inside the building. Second, in this report the most important is not actually the results as numbers but actually the model and the accuracy of the thermodynamic equations it uses in order to produce the results. This electrical load profile is described in a more detail in the literature review chapter in the section energy consumption in a SM. The problem with these data is that these load profiles are the total electrical load profiles (aren’t separated) and are 41
  42. 42. not fitted to our model which takes as input for every month the 24 hour electrical- only, cooling and heating loads separately. For that reason these profiles were separated manually, without great detail but following a constant logic. From the research in energy consumption in shopping malls the energy breakdown was:  40-60% HVAC  20-30% LIGHTING  5-10% other appliances  3-4% lifts The heating demand in a SM due to the great thermal loads from the lights and the high people densities during the working hours is mainly in early morning or late afternoon hours with bigger needs during the winter months. On the other hand cooling demand for the same reasons is peaked during the hours 12:00 to 15:00 with greater effect on summer months, when and the outside temperature comes to be added in the high internal thermal loads. Finally the electrical-only loads are almost stable during the 24 hours and the 12 months. Mainly for the previous reasons the breakdown of the total electrical load to the electrical-only, cooling and heating follow the below separation rules (For each hour of a day, every month and season the sum of the electrical-only, cooling and heating percentages must have sum the 100% of the total electrical load ): Summer months: 1) Electrical-only loads (percentages to the total):  From the hours 22:00 to 6:00, 50%  All the rest hours of the day, 40% 2) Cooling loads:  From the hours 22:00 to 6:00, 50%  From the hours 6:00 to 10:00, 30% 42
  43. 43.  From the hours 10:00 to 18:00, 45%  From the hours 18:00 to 22:00, 35% 3) Heating loads:  From the hours 22:00 to 6:00, 0%  From the hours 6:00 to 10:00, 30%  From the hours 10:00 to 18:00, 15%  From the hours 18:00 to 22:00, 25% Winter months: 4) Electrical-only loads (percentages to the total):  All the hours, 40% 5) Cooling loads:  From the hours 22:00 to 6:00, 30%  From the hours 6:00 to 10:00, 20%  From the hours 10:00 to 18:00, 30%  From the hours 18:00 to 22:00, 20% 6) Heating loads:  From the hours 22:00 to 6:00, 30%  From the hours 6:00 to 10:00, 40%  From the hours 10:00 to 18:00, 30%  From the hours 18:00 to 22:00, 40% By following the previous rules the SM detailed profiles are depicted in figures 24, 25, and 26. 43
  44. 44. SM Electrical Load 1400 January 1200 February March 1000 End-use load (KW) April 800 May June 600 July 400 August 200 September October 0 November 0 5 10 15 20 25 30 December Hours Figure 23, SM Electrical load (F. Javier Rubio, 2001) SM Electrical-only demand 600 January 500 February March End-use load (KW) 400 April May 300 June 200 July August 100 September October 0 November 0 5 10 15 20 25 30 December Hours Figure 24, SM Electrical-only demand 44
  45. 45. SM Cooling demand January 600 February March 500 April End-use load (KW) May 400 June 300 July August 200 September 100 October November 0 December 0 5 10 15 20 25 30 Hours Figure 25, SM Cooling demand SM Heating demand January 450 February 400 March 350 April End-use load (KW) 300 May 250 June 200 July 150 August 100 September October 50 November 0 December 0 5 10 15 20 25 30 Hours Figure 26, SM heating demand 45
  46. 46. 3.3 Tariffs inputs Tariffs are a key input to our mathematical model. The two market inputs that will be explained in the next two sub-sections in detail are the natural gas prices and the grid electricity prices. 3.3.1 Natural gas prices Natural gas prices are a commodity that don’t change price so often during the month, has small volatility, and thus we take average monthly prices in contrast with electricity prices which change in a few minutes basis. The natural gas prices in $ per MMBTU were taken from the Energy Information Administration website (http://www.eia.doe.gov/) which is the official energy statistics from the U.S. government. For the case of the SM the commercial prices of 2008 were used (Release Date: 8/29/2008) and represented in the below figure 27, 28. Due to the fact that the NG prices for 2008 are not completed for this year (data are up to June 2008), but also it wasn’t wise to use the 2007 (this year prices are lower) for the rest of the year (July to December) an analogy was used in this way: we calculated the percentage that 2008 prices (up to June) are higher from 2007 prices and we added this to the 2007 prices for the rest of the year. NG price in $ per MMBTU 2008 2007 January 11.07 11.14 february 11.37 11.24 March 11.76 11.82 April 12.45 11.51 May 13.23 11.51 June 14.41 11.87 July 12.74 11.63 August 12.24 11.18 September 11.94 10.9 Octomber 11.83 10.8 November 12.09 11.04 December 12.07 11.02 Figure 27, monthly natural gas prices in $ per MMBTU for the calendar years 2007, 2008 46
  47. 47. Natural gas Price in $ per MMBTU 16 14 12 $ per MMBTU 10 8 6 4 2 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Figure 28, graph representation for natural gas prices in $ per MMBTU for 2008 3.3.2 Electricity prices (Grid) After the explanation of the NG prices the next important tariff input to the model is electricity price purchased from the grid to the customer. For this model the electricity price is calculated as shown in the figure 29. According to Peter Williams and Goran Strbac, in their book costing and pricing of Electricity distribution services the consumer final electricity price consisted by 51% from the electricity generation cost and the rest 49% from the transmission, distribution, and supply cost. The generation cost will be assumed to be the spot market electricity prices for this year (2008). These spot market prices will be taken from the Elexon BSC website (http://www.elexon.co.uk/), and more specifically in the section Pricing data the market index data for the year 2008 (http://www.elexon.co.uk/marketdata/PricingData/MarketIndexData/default.aspx). Like with the natural gas and here because of the fact that this year is not ended yet, the real 2008 data will be from January to June and the rest months July to December will be calculated as before: find the percentage that 2008 prices (up to June) are higher from 2007 prices, then add this to the 2007 prices for the rest of the year and finally keep these new prices as the rest 2008 values. These market spot prices are depicted in figure 30. 47
  48. 48. As soon as the spot market prices calculated, then the annual average price is calculated and this value is multiplied by 49/51 in order to find the distribution, transmission and supply payment. This parameter is called DistrPay in the GAMS and its value is 0.13936531 $/KWh (for 2008). So each time the customer purchases one KWh, the price will be payed back to the grid consisted of the spot market price for the exact time of the purchase and the constant DistrPay. Using this method the final electricity prices for the whole year depicted in figure 31. Figure29. Contribution of distribution costs to electricity bill (Williams P. a., 2001) 48
  49. 49. Spot market electricity price January 0.45 february 0.4 March 0.35 April 0.3 May $ per KWh 0.25 June 0.2 July 0.15 August 0.1 September 0.05 Octomber 0 November 0 5 10 15 20 25 30 December Hour Figure 30, Spot market electricity prices Grid electricity price with the distribution January company revenue february 0.6 March 0.5 April May 0.4 June $ per KWh 0.3 July August 0.2 September 0.1 Octomber November 0 December 0 5 10 15 20 25 30 Hour Figure 31, Grid electricity price with the distribution company revenue 49
  50. 50. 4. Mathematical Model 4.1 Introduction In this part, the mathematical model will be presented and explained but also and the reasons behind this venture. The results which are presented are intended more to show: the great usage of GAMS in solving difficult optimization problems, the possible savings that can be achieved by the optimization of the energy systems (combination of DG and grid) in a shopping mall (extended in a microgrid) and not the actual numbers of the final energy cost of a shopping mall and the actual carbon savings. Improvements must be undertaken in the tariffs in order the model to use an accurate electricity tariff system, in the load profiles which are key input to the model (must be monitored a real SM for a year and calculated the accurate electricity, heating and cooling profiles), and also the technology information (more accurate costs and a more accurate thermodynamic model which will take into account the efficiency drops etc). Finally we can say, that given all the foresaid inputs (customer demand, electricity/NG tariffs and technology information) the model can give back some strategic results about the way DG technologies and Grid must be combined (which DG must installed) and work (when this capacity will operate during the year) in order to have some energy, money and carbon savings while we meet the constant customer demand. 4.2 Mathematical Programming We use mathematical programming in order to build the energy models. H. Paul Williams gives a definition for the mathematical programming (Williams H. , 1999): Mathematical programming has a sense of planning for the purpose of optimization, it is a mathematical problem regarding to maximizing or minimizing something which is known as objective function and it has to satisfy the conditions called constraints. The mathematical programming models are able to be classified as linear programming models, non-linear programming models and integer programming models.  Linear Programming Model (LP): Linear programming is the optimization problem in which the objective functions and the constraints are all linear. 50
  51. 51.  Non-linear Programming (NLP): Non-linear programming is the optimization problem in which at least one of the objective functions or the constraints is a non-linear function.  Mixed-integer Programming (MIP): Mixed-integer programming is the optimization problem that has both continuous variables together with integer variables. It can be mixed-integer linear programming (MILP) or mixed- integer non-linear programming (MINLP).  Mixed-integer linear Programming (MILP): Mixed-Integer Programming (MIP) methods (L.T. Biegler, 1997) are suitable for modeling and analyzing buildings energy systems towards design, investment planning and optimization: this established algorithmic framework fulfills the requirements and captures the complexities of an investment planning procedure, by considering the superstructure of all alternatives, representing all possible choices for a system by binary (0–1) variables, while all the physical and economic quantities are expressed as continuous variables. All logical and physical relations are translated into equality or inequality constraints. The best plan is derived by conducting an optimization for a specific objective function (Liu Pei, 2007). 4.3 General Algebraic Modeling System (GAMS) General Algebraic Modeling System (GAMS) is multipurpose optimization software which is particularly designed for modeling linear, non-linear and mixed integer optimization (MIP) problems. Most of the researchers use GAMS for solving large and complex mixed integer linear programming (MILP) problems. Of course GAMS can do much more than these but is not in the current needs of this subject. The basic reasons GAMS selected for this optimization are: 51
  52. 52.  Offer a high level language, for the illustration of large, difficult and complex models.  Provide easy and safe changes in the specification of the model.  Allows unambiguous statements of algebraic relationships  Allows comments in the model which are independent to the model solutions. 4.4 Model Description In this current model, there are two input fuels: natural gas and electricity from the grid. At the other end of the model there are three end uses that can be met: electrical- only, cooling and heating loads. The models objective function is to minimize the cost of meeting the Shopping malls energy demand for one year (while taking into account the carbon emissions) by optimizing the usage of different distributed generation technologies and the power from grid. In order to reach this objective the model must answer the following questions:  If it is economically feasible, which DG technologies must be adopted?  The chosen DG technologies in which capacity will be installed?  How this capacity must be operated during day/year in order to minimize the energy cost while meeting at all times the customer demand?  It is more economically for the customer to disconnect for the grid or there are profit opportunities by selling electricity back to the grid (especially the times of the high demand / high price)? The model inputs are:  The SM electricity-only, cooling and heating load profiles,  The hourly spot electricity prices for the year 2008/2007 2 (with the payment of distribution company) and the monthly spot natural gas prices for the same year, 52

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